RadxTools for assessing tumor treatment response on imaging

用于评估影像学肿瘤治疗反应的 RadxTools

• 批准号: 10477947
• 负责人: Pallavi Tiwari
• 金额: $ 36.57万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10477947
• 关键词: 3-Dimensional; Accounting; Address; Aftercare; Alternative Therapies; American; American College of Radiology Imaging Network; Benchmarking; Benign; Biophysics; Biopsy; Brain Neoplasms; Cancer Patient; Clinical; Clinical Pathology; Communities; Complication; Data; Data Set; Detection; Development; Diagnosis; Diagnostic Imaging; Disease; Eastern Cooperative Oncology Group; Educational workshop; Evaluation; Funding; Growth; Histopathology; Image; In complete remission; Informatics; Learning; Lesion; Magnetic Resonance Imaging; Malignant neoplasm of brain; Measures; Mesorectal; Morphologic artifacts; Morphology; Neoadjuvant Therapy; Noise; Oncologist; Oncology; Operative Surgical Procedures; Optics; Pathologic; Pathology; Patients; Phenotype; Positioning Attribute; Professional Organizations; Progressive Disease; Quality Control; Radiation; Radiation necrosis; Radiation therapy; Radiology Specialty; RadxTools; Recording of previous events; Rectal Cancer; Recurrence; Recurrent tumor; Residual Tumors; Residual state; Resolution; Resources; Site; Societies; Southwest Oncology Group; Specimen; Surface; The Cancer Imaging Archive; Tissues; Treatment Protocols; Tumor Markers; Validation; Variant; base; cancer recurrence; cancer therapy; chemoradiation; clinical imaging; cohort; computerized; data resource; digital; feature extraction; follow-up; imaging informatics; in vivo; in vivo imaging; informatics tool; novel; open source; photonics; prototype; radiation effect; radiological imaging; radiomics; responders and non-responders; response; routine imaging; side effect; success; tool; treatment effect; treatment response; tumor; visual information; working group; 3-Dimensional; Accounting; Address; Aftercare; Alternative Therapies; American; American College of Radiology Imaging Network; Benchmarking; Benign; Biophysics; Biopsy; Brain Neoplasms; Cancer Patient; Clinical; Clinical Pathology; Communities; Complication; Data; Data Set; Detection; Development; Diagnosis; Diagnostic Imaging; Disease; Eastern Cooperative Oncology Group; Educational workshop; Evaluation; Funding; Growth; Histopathology; Image; In complete remission; Informatics; Learning; Lesion; Magnetic Resonance Imaging; Malignant neoplasm of brain; Measures; Mesorectal; Morphologic artifacts; Morphology; Neoadjuvant Therapy; Noise; Oncologist; Oncology; Operative Surgical Procedures; Optics; Pathologic; Pathology; Patients; Phenotype; Positioning Attribute; Professional Organizations; Progressive Disease; Quality Control; Radiation; Radiation necrosis; Radiation therapy; Radiology Specialty; RadxTools; Recording of previous events; Rectal Cancer; Recurrence; Recurrent tumor; Residual Tumors; Residual state; Resolution; Resources; Site; Societies; Southwest Oncology Group; Specimen; Surface; The Cancer Imaging Archive; Tissues; Treatment Protocols; Tumor Markers; Validation; Variant; base; cancer recurrence; cancer therapy; chemoradiation; clinical imaging; cohort; computerized; data resource; digital; feature extraction; follow-up; imaging informatics; in vivo; in vivo imaging; informatics tool; novel; open source; photonics; prototype; radiation effect; radiological imaging; radiomics; responders and non-responders; response; routine imaging; side effect; success; tool; treatment effect; treatment response; tumor; visual information; working group

ABSTRACT: Over 1.6 million patients in the U.S. annually undergo chemo- or radiation- as first-line cancer therapy. After therapy, the most significant challenge for oncologists is identifying non-responders (those with residual or progressive disease), which could allow them to be switched to alternative therapies. Similarly, if those with stable or regressing disease were identified early and reliably, patients could avoid unnecessary and highly morbid surgeries or biopsies for disease confirmation. Unfortunately, expert assessment of post-treatment imaging is challenging, as residual disease is visually confounded with benign treatment-induced changes on imaging. There is hence a critical need for dedicated radiomic (computerized feature extraction from imaging) and informatics approaches to enable reliable post-treatment tumor assessment. Such tools will need to account for: (1) Limited well-curated data resources with deeply annotated pathology-validated radiographic datasets, for discovery and validation of new imaging and radiomic markers for post-treatment characterization in vivo; (2) Need for specialized radiomics tools that specifically quantify morphological perturbations in response to shrinkage/growth of the lesion for identifying progressive disease (versus benign confounders), despite presence of treatment-induced artifacts (exacerbated noise, reduced contrast, poor resolution); and (3) Lack of comprehensive quality control (QC) tools to identify which of a plethora of radiomic features are both discriminable as well as generalizable to variations between sites and scanners. To address these challenges, we propose RadxTools, a new image informatics toolkit comprising three modules: (a) RadQC to enable quality control of radiomics features across multi-site imaging cohorts, (b) RadTx comprising new radiomics tools which capture local surface morphometric changes and subtle structural deformations unique to tumor response on post-treatment imaging, and (c) RadPathFuse for creating deeply annotated learning sets by spatially mapping post-treatment changes from ex vivo surgically excised histopathology specimens onto pre-operative in vivo imaging. RadxTools will be evaluated in the context of post-treatment characterization for use cases in distinguishing (a) radiation effects from cancer recurrence for brain tumors; and (b) complete/partial vs incomplete chemoradiation response for rectal cancers. Deliverables and Dissemination: Our team has had a successful history of disseminating informatics tools (>1000 downloads), including our most recent release of RadTx which has been integrated into 3 informatics platforms. By organizing community resources and targeted workshops, as well as releasing highly curated data cohorts, our team is uniquely positioned to disseminate RadxTools to the radiomics/imaging community, professional societies, and oncology working groups. Our deliverables will include tool prototypes as modules within 5 QIN/ITCR-funded platforms (3D Slicer, MeVisLab, Sedeen, CapTk, QIFP) for widespread dissemination to targeted end-user communities, in addition to deeply annotated learning sets assembled through the 2 use-cases in this project.

摘要：美国每年有超过160万患者接受化学或放射线作为一线癌症 治疗。治疗后，肿瘤学家最重大的挑战是确定非反应者（患有 残留或进行性疾病），这可以使它们被切换到替代疗法。同样，如果 那些患有稳定或消退疾病的人早点发现，可靠地，患者可以避免不必要的 高病态的手术或活检以进行疾病确认。不幸的是，治疗后的专家评估 成像是具有挑战性的，因为残留疾病在视觉上与良性治疗引起的变化有关 成像。因此，非常需要专用的放射线（从成像中提取计算机化特征） 以及提供可靠的治疗后肿瘤评估的信息学方法。这样的工具需要考虑 for：（1）有限术的数据资源有限，带有注释的病理学验证射线照相数据集，用于 在体内进行后处理表征的新成像和放射线标记的发现和验证； （2） 需要专门量化形态扰动的专业放射线学工具。 尽管存在 治疗诱导的伪影（加剧的噪声，降低对比度，分辨率差）； （3）缺乏 综合质量控制（QC）工具，以识别哪些放射线特征的哪些是 可以区分和概括地对位点和扫描仪之间的变化。为了应对这些挑战， 我们提出了Radxtools，这是一个新的图像信息学工具包，包括三个模块：（a）RadQC启用质量 控制多站点成像队列中的放射组功能，（b）RADTX，包括新的放射线学工具 捕获局部表面的形态变化和微妙的结构变形，肿瘤反应在 治疗后成像，以及（c）通过空间映射创建深层注释的学习集的RadPathFuse 在体内手术切除的组织病理学标本中的治疗后治疗变化到体内术前的术 成像。 Radxtools将在对用例的治疗表征的背景下进行评估 区分（a）辐射效应与脑肿瘤的癌症复发； （b）完整/部分vs 直肠癌的不完全化学放射反应。可交付成果和传播：我们的团队有一个 传播信息学工具的成功历史（> 1000个下载），包括我们最近的发布 已集成到3个信息平台中的RADTX。通过组织社区资源并针对 研讨会以及发布高度精心策划的数据队列，我们​​的团队在传播方面具有独特的位置 Radxtools to radiomics/Imaging社区，专业社会和肿瘤学工作组。我们的 可交付成果将在5个QIN/ITCR资助的平台（3D Slicer，Mevislab，Mevislab， Sedeen，Captk，QIFP），除了深入的最终用户社区，以广泛的传播 通过该项目的两个用例组装的注释学习集。